In the age of computers, one task that remains a problem is the ability for computers to understand our language. For example, who has not struggled with automated telephone systems?
If you have a regional accent or speak too quickly or slowly, if your pronunciation isn’t clear, or if there is background noise, the system often fails to work properly.
The reason for this is that until now the computer programs that have been used rely on processes that are particularly sensitive to perturbations.
When computers process language, they primarily attempt to recognize characteristic features in the frequencies of the voice in order to recognize words.
“It is likely that the brain uses a different process,” says Stefan Kiebel from the Leipzig Max Planck Institute for Human Cognitive and Brain Sciences.
Kiebel posits the analysis of temporal sequences plays an important role in this. “Many perceptual stimuli in our environment could be described as temporal sequences.” Music and spoken language, for example, are composed of sequences of different length which are hierarchically ordered.
According to the scientist’s hypothesis, the brain classifies the various signals from the smallest, fast-changing components (e.g., single sound units like ‘e’ or ‘u’) up to big, slow-changing elements (e.g., the topic).
The significance of the information at various temporal levels is probably much greater than previously thought for the processing of perceptual stimuli. “The brain permanently searches for temporal structure in the environment in order to deduce what will happen next,” Kiebel explains.
In this way, the brain can, for example, often predict the next sound units based on the slow-changing information. Thus, if the topic of conversation is the hot summer, ‘su…’ will more likely be the beginning of the word ‘sun’ than the word ‘supper.’
To test this hypothesis, the researchers constructed a mathematical model which was designed to imitate, in a highly simplified manner, the neuronal processes which occur during the comprehension of speech. Neuronal processes were described by algorithms which processed speech at several temporal levels.
The model succeeded in processing speech; it recognized individual speech sounds and syllables. In contrast to other artificial speech recognition devices, it was able to process speeded-up speech sequences. Furthermore it had the brain’s ability to ‘predict’ the next speech sound. If a prediction turned out to be wrong because the researchers made an unfamiliar syllable out of the familiar sounds, the model was able to detect the error.
The ‘language’ with which the model was tested was simplified – it consisted of the four vowels a, e, i and o, which were combined to make ‘syllables’ consisting of four sounds. “In the first instance we wanted to check whether our general assumption was right,” Kiebel explains.
With more time and effort, consonants, which are more difficult to differentiate from each other, could be included, and further hierarchical levels for words and sentences could be incorporated alongside individual sounds and syllables. Thus, the model could, in principle, be applied to natural language.
“The crucial point, from a neuroscientific perspective, is that the reactions of the model were similar to what would be observed in the human brain,” Stefan Kiebel says. This indicates that the researchers’ model could represent the processes in the brain. At the same time, the model provides new approaches for practical applications in the field of artificial speech recognition.
Source: Max Planck Institute